Machine Learning-based Energy-Aware Offloading in Edge-Cloud Vehicular Networks

Leila Ismail, Huned Materwala

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)


A vehicular network underpinned by the 3-tier vehicle-edge-cloud infrastructure enables an efficient and safer travel experience. The compute-intensive vehicular applications are often offloaded to the edge and/or cloud servers to enhance the applications' Quality of Services (QoS). The underlying edge-cloud servers consume a high among of energy. Consequently, it becomes crucial to optimizing energy consumption in the offloading process. Current energy-efficient offloading strategies in 2-tier vehicle-edge infrastructure, do not account for cloud computing energy consumption. In this paper, we address this void by proposing a machine learning-based energy-aware offloading algorithm, which optimizes the energy of the edge-cloud computing platform. The offloading strategy is enabled by the Support Vector Machine (SVM) regression model machine learning algorithm used for the edge-cloud power prediction. The experimental results show that the proposed algorithm is a promising approach in energy savings.


  • Cloud computing
  • Computation offloading
  • Edge computing
  • Energy-efficiency
  • Machine learning
  • Queuing theory
  • Support Vector Machine (SVM)
  • Vehicular network

ASJC Scopus subject areas

  • General Computer Science


Dive into the research topics of 'Machine Learning-based Energy-Aware Offloading in Edge-Cloud Vehicular Networks'. Together they form a unique fingerprint.

Cite this